Nonlinear Model Predictive Control for Heavy-Duty Hybrid Electric Vehicles Using Random Power Prediction Method
Open accessAbstract
A primary challenge to the implementation of hybrid electric vehicles (HEVs) is the design of the energy management strategy for the vehicle. Most conventional strategies have been designed for passenger vehicles using rule-based or optimization-based control strategies that rely on navigation support; therefore, the optimal performance of heavy-duty HEVs that lack navigation support cannot be achieved using conventional strategies. In this study, we propose a nonlinear model predictive control (NMPC) for heavy-duty HEVs based on a random power prediction method. To obtain the models of multiple power sources, we analyzed the structure and powertrain of the vehicle using mathematical modeling methods. To account for the lack of navigation support, we used the data-driven prediction method by combining the grey model and Markov chain methods to obtain higher-accuracy ultra-short-term power prediction. Considering the predicted disturbance power, we established a multi-objective optimization function with explicit constraints to optimize fuel consumption, bus voltage, and battery state of charge. Under these constraints, a nonlinear programming problem based on the NMPC could be restricted to find an optimal numerical solution in real time. We validated the control strategy on a hardware-in-the-loop simulation platform and compared its results with those obtained using thermostat control, fuzzy, and dynamic programming approaches. The proposed control strategy achieved a considerably better all-round performance than rule-based control strategies; moreover, the results were considerably similar compared with those of offline global optimization strategies. Furthermore, the proposed method achieved excellent real-time operation capability, thereby providing a valuable reference for practical engineering applications.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The four driving cycles FTP75, LA92, UDDS, and HWFET are extensively utilized in scholarly research focused on EV simulation and performance evaluations.
“Each of these driving cycles is extensively utilized in scholarly research focused on EV simulation and performance evaluations.(Chen et al., 2020;Peng et al., 2019;Karavalakis et al., 2013;Liu et al., 2016)”
From State-of-charge estimation of commercial electric vehicles by using battery-aware transformer network algorithms (2025) · cites this paper as Chen et al., 2020
In the Grey Markov Chain Framework and Results and Discussions (page numbers unavailable), Chen et al. use HWFET to evaluate a simulated heavy-duty hybrid electric vehicle. The cited passages do not establish extensive use across scholarly research or address FTP75, LA92, and UDDS; the other cycle used here is HUDDS, not UDDS. (LLM, conf=0.85)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
MPC-Based Energy Management of a Power-Split Hybrid Electric Vehicle
Borhan, H., Vahidi, A., Phillips, A. M., et al. (2011) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2011.2134852 · Cited by 741 · see citing works
Tran, D.-D., Vafaeipour, M., Baghdadi, M. E., Barrero, R., Mierlo, J. V., & Hegazy, O. (2019) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2019.109596 · Cited by 567 · see citing works
Cairano, S. D., Bernardini, D., Bemporad, A., & Kolmanovsky, I. V. (2013) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2013.2272179 · Cited by 447 · see citing works
Research progress on power battery cooling technology for electric vehicles
Lu, M., Zhang, X., Ji, J., Xu, X., & Zhang, Y. (2019) · Journal of Energy Storage · doi:10.1016/j.est.2019.101155 · Cited by 435 · see citing works
Deng, C., & Wen, C. (2020) · IEEE Transactions on Control of Network Systems · doi:10.1109/tcns.2020.2972601 · Cited by 395 · see citing works
Song, Z., Li, J., Han, X., et al. (2014) · Applied Energy · doi:10.1016/j.apenergy.2014.06.087 · Cited by 379 · see citing works
Energy and Battery Management of a Plug-In Series Hybrid Electric Vehicle Using Fuzzy Logic
Li, S., Sharkh, S. M., Walsh, F. C., & Zhang, C. (2011) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2011.2165571 · Cited by 354 · see citing works
Hu, X., Murgovski, N., Johannesson, L. M., et al. (2013) · Applied Energy · doi:10.1016/j.apenergy.2013.06.056 · Cited by 318 · see citing works
Song, K., Wang, X., Li, F., Sorrentino, M., & Zheng, B. (2020) · Energy · doi:10.1016/j.energy.2020.118064 · Cited by 221 · see citing works
Distributed Resilient Control for Energy Storage Systems in Cyber–Physical Microgrids
Deng, C., Wang, Y., Wen, C., et al. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.2981549 · Cited by 216 · see citing works
A new multivariable grey prediction model with structure compatibility
Zeng, B., Duan, H., & Zhou, Y. (2019) · Applied Mathematical Modelling · doi:10.1016/j.apm.2019.05.044 · Cited by 204 · see citing works
Hu, J., Liu, D., Du, C., et al. (2020) · Energy · doi:10.1016/j.energy.2020.117298 · Cited by 189 · see citing works
Estimation of the ECMS Equivalent Factor Bounds for Hybrid Electric Vehicles
Rezaei, A., Burl, J. B., & Zhou, B. (2017) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2017.2740836 · Cited by 186 · see citing works
Fuel consumption optimization for smart hybrid electric vehicle during a car-following process
Li, L., Wang, X., & Song, J. (2016) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2016.03.002 · Cited by 178 · see citing works
Abdel-Monem, M., Trad, K., Omar, N., Hegazy, O., den Bossche, P. V., & Mierlo, J. V. (2016) · Energy · doi:10.1016/j.energy.2016.12.110 · Cited by 178 · see citing works
A novel location-routing problem in electric vehicle transportation with stochastic demands
Zhang, S., Chen, M., & Zhang, W. (2019) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2019.02.167 · Cited by 154 · see citing works
Predictive vehicle-following power management for plug-in hybrid electric vehicles
Xie, S., Hu, X., Liu, T., et al. (2018) · Energy · doi:10.1016/j.energy.2018.10.129 · Cited by 153 · see citing works
Resilient Control Design for Lateral Motion Regulation of Intelligent Vehicle
Chang, X., Liu, Y., & Shen, M. (2019) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2019.2946895 · Cited by 139 · see citing works
Analytic Solutions to the Dynamic Programming Subproblem in Hybrid Vehicle Energy Management
Larsson, V., Johannesson, L. M., & Egardt, B. S. (2014) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2014.2329864 · Cited by 135 · see citing works
Chang, Y., Wang, Y. Q., Alsaadi, F. E., & Zong, G. (2019) · International Journal of Adaptive Control and Signal Processing · doi:10.1002/acs.3052 · Cited by 131 · see citing works
A control-oriented simulation model of a power-split hybrid electric vehicle
Cipek, M., Pavković, D., & Petrić, J. (2012) · Applied Energy · doi:10.1016/j.apenergy.2012.07.006 · Cited by 117 · see citing works
Optimal Energy Management and Sizing of a Dual Motor-Driven Electric Powertrain
Hu, X., Li, Y., Lv, C., et al. (2018) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2018.2879225 · Cited by 116 · see citing works
Guo, H., Wang, X., & Li, L. (2019) · Energy Conversion and Management · doi:10.1016/j.enconman.2019.111972 · Cited by 103 · see citing works
Chang, X., & Liu, Y. (2020) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2020.3008222 · Cited by 103 · see citing works
Ma, L., Xu, N., Huo, X., & Zhao, X. (2020) · Nonlinear Analysis Hybrid Systems · doi:10.1016/j.nahs.2020.100908 · Cited by 102 · see citing works
Energy Management Improvement of Hybrid Electric Vehicles via Combined GPS/Rule-Based Methodology
Ramadan, H. S., Becherif, M., & Claude, F. (2017) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2017.2650146 · Cited by 92 · see citing works
Lai, X., Wang, S., He, L., Zhou, L., & Zheng, Y. (2020) · Journal of Energy Storage · doi:10.1016/j.est.2019.101106 · Cited by 90 · see citing works
Bi-level optimal sizing and energy management of hybrid electric propulsion systems
Zhu, J., Chen, L., Wang, X., & Yu, L. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.114134 · Cited by 89 · see citing works
Self-citationDjerioui, A., Houari, A., Zeghlache, S., et al. (2019) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2019.07.060 · Cited by 86 · see citing works
Fuzzy‐based blended control for the energy management of a parallel plug‐in hybrid electric vehicle
Denis, N., Dubois, M. R., & Desrochers, A. (2014) · IET Intelligent Transport Systems · doi:10.1049/iet-its.2014.0075 · Cited by 85 · see citing works
Xing, Y., Lv, C., Cao, D., & Lu, C. (2020) · Applied Energy · doi:10.1016/j.apenergy.2019.114471 · Cited by 83 · see citing works
Robust Model Predictive Control With Simplified Repetitive Control for Electrical Machine Drives
Liu, Y., Cheng, S., Ning, B., et al. (2018) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2018.2857837 · Cited by 73 · see citing works
Wang, B., Chen, W., Wang, J., et al. (2020) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2020.2991695 · Cited by 68 · see citing works
Esfandyari, M., Yazdi, M. R. H., Esfahanian, V., Masih‐Tehrani, M., Nehzati, H., & Shekoofa, O. (2019) · Journal of Energy Storage · doi:10.1016/j.est.2019.100758 · Cited by 67 · see citing works
Optimising lithium-ion cell design for plug-in hybrid and battery electric vehicles
Campbell, I. D., Gopalakrishnan, K., Marinescu, M., Torchio, M., Offer, G. J., & Raimondo, D. M. (2019) · Journal of Energy Storage · doi:10.1016/j.est.2019.01.006 · Cited by 61 · see citing works
Torque-Leveling Threshold-Changing Rule-Based Control for Parallel Hybrid Electric Vehicles
Li, X., & Evangelou, S. A. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2019.2916720 · Cited by 54 · see citing works
Optimal charging for general equivalent electrical battery model, and battery life management
Abdollahi, A., Han, X., Raghunathan, N., Pattipati, B., Balasingam, B., Pattipati, K. R., Bar‐Shalom, Y., & Card, B. (2016) · Journal of Energy Storage · doi:10.1016/j.est.2016.11.002 · Cited by 53 · see citing works
Hadroug, N., Hafaifa, A., Kouzou, A., et al. (2016) · Energy · doi:10.1016/j.energy.2016.11.099 · Cited by 50 · see citing works
Xie, H., Tian, G., Du, G., Huang, Y. Z., Chen, H., Zheng, X., & Luan, T. H. (2018) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2018.2868965 · Cited by 42 · see citing works
Wang, Y., Wu, Z., Chen, Y., et al. (2018) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2018.09.018 · Cited by 42 · see citing works
Rizoug, N., Mesbahi, T., Sadoun, R., et al. (2018) · Energy Efficiency · doi:10.1007/s12053-017-9602-8 · Cited by 41 · see citing works
Metallurgical characteristics of armour steel welded joints used for combat vehicle construction
Magudeeswaran, G., Balasubramanian, V. R., & Reddy, G. M. (2018) · Defence Technology · doi:10.1016/j.dt.2018.07.021 · Cited by 40 · see citing works
A novel multivariate grey model for forecasting the sequence of ternary interval numbers
Zeng, X., Shu, L., Yan, S., et al. (2018) · Applied Mathematical Modelling · doi:10.1016/j.apm.2018.12.020 · Cited by 40 · see citing works
Wang, J., Cai, Y., Chen, L., et al. (2020) · International Journal of Energy Research · doi:10.1002/er.5264 · Cited by 40 · see citing works
Self-citationAnalytical Solution to Energy Management Guaranteeing Battery Life for Hybrid Trucks
Pham, T. H., Kessels, J. T. B. A., van den Bosch, P. P. J., & Huisman, R. G. M. (2015) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2015.2480745 · Cited by 38 · see citing works
Zhao, B., Lv, C., & Hofman, T. (2019) · Automotive Innovation · doi:10.1007/s42154-019-00059-z · Cited by 33 · see citing works
A Distributed Optimization Approach for Complete Vehicle Energy Management
Romijn, T. C. J., Donkers, M. C. F., Kessels, J., et al. (2018) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2018.2789464 · Cited by 28 · see citing works
Chen, Z., Liu, W., Yang, Y., et al. (2015) · Mathematical Problems in Engineering · doi:10.1155/2015/368769 · Cited by 25 · see citing works
Li, S., Hu, M., Gong, C., et al. (2018) · Energies · doi:10.3390/en11061531 · Cited by 24 · see citing works
Li, Y., & Jiao, X. (2019) · Science Progress · doi:10.1177/0036850419874992 · Cited by 22 · see citing works
Fries, M., Kruttschnitt, M., & Lienkamp, M. (2018) · IEEE Transactions on Industry Applications · doi:10.1109/tia.2018.2823693 · Cited by 18 · see citing works
Yang, W., Yang, J., Liang, J., et al. (2018) · IET Intelligent Transport Systems · doi:10.1049/iet-its.2018.5054 · Cited by 18 · see citing works
A Simplified Li-ion Battery SOC Estimating Method
Zhang, X., Wang, X., Zhang, W., et al. (2016) · Transactions on Electrical and Electronic Materials · doi:10.4313/teem.2016.17.1.13 · Cited by 12 · see citing works
A dynamic Markov chain prediction model for delay-tolerant networks
Jeon, I.-K., & Lee, K.-W. (2016) · International Journal of Distributed Sensor Networks · doi:10.1177/1550147716666662 · Cited by 10 · see citing works
Nonlinear Model Predictive Control for Series-Parallel Hybrid Electric Buses
Liu, B., Wang, T., Wang, H., et al. (2019) · IEEE Access · doi:10.1109/access.2019.2941082 · Cited by 10 · see citing works
Sample caching Markov chain Monte Carlo approach to boson sampling simulation
Liu, Y., Xiong, M., Wu, C., Wang, D., Liu, Y., Ding, J., Huang, A., Fu, X., Qiang, X., Xu, P., et al. (2020) · New Journal of Physics · doi:10.1088/1367-2630/ab73c4 · Cited by 8 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Mu, B.-Q., Doukhi, O., Wang, L., & Lee, D.-J. (2025) · Journal of Computational Design and Engineering · doi:10.1093/jcde/qwaf042 · Cited by 5 · see citing works
- Authors
- Chen, L., Liao, Z., & Ma, X.
- Venue
- IEEE Access
- Published
- 2020
- Volume
- 8
- Pages
- 202819-202835
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W3103698355
- Cited by
- 19
- References
- 56
- Citations by year
- 2026: 1 · 2025: 7 · 2024: 2 · 2023: 4 · 2022: 3 · 2021: 2
- FWCI
- 0.83
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (51507190)
- Weapons and Equipment Pre-Research Project of China (301051102)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Electric and Hybrid Vehicle Technologies
- Physical Sciences › Engineering › Automotive Engineering › Vehicle Dynamics and Control Systems
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Vehicles and Infrastructure
- Keywords
- nonlinear model predictive control, energy management strategy, grey prediction model, Markov chains, multi-objective optimization, fuel consumption, battery state of charge, bus voltage, real-time optimization, hardware-in-the-loop
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
